صوت Elon Musk الاحترافي بالذكاء الاصطناعي لإنشاء المحتوى

Look, the thing about autonomous vehicles is, they're probably going to be way safer than human drivers, maybe 10x safer. But there's still this interesting challenge of making them understand human behavior, which is, you know, pretty chaotic and unpredictable.

Look, the fundamental problem with current rocket systems is that they're incredibly inefficient. It's like using a 1970s computer in 2024. We need to revolutionize the whole architecture, make it fully reusable, real-time responsive. That's what we're working on.

Look, the thing about artificial intelligence is, you know, it's not really about having the most complex algorithms. I mean, what matters is solving real problems. Like, if you look at successful AI companies, they're focused on actual applications, not just theoretical stuff.

Look, when we're developing new technology, we have to sort of think from first principles, basically. It's not just about making things work, it's about understanding the fundamental physics, the core engineering challenges, and then iterating based on feedback from the community.

Look at artificial intelligence like a tool that amplifies human capability. If you think about it like a neural network, you want to find the optimal connection points where human creativity intersects with computational power. That's where the real innovation happens.

Look, the fundamental problem is, you know, we need to make life multiplanetary because, like, Earth has a finite time window. Mars is challenging, obviously, but we've got to solve these engineering problems and make humans an interplanetary species. That's the only logical path forward.

Look, the problem with current AI regulation is that it's like trying to fix a quantum computer with stone age tools. We need to completely rebuild the framework, not just patch it. So what we really need is a fundamental restructuring of how we approach AI safety.

So like we're trying this new battery chemistry, and you know, the first few attempts basically exploded, which wasn't great obviously. But we learned a ton from those failures, and now we're actually getting pretty good efficiency numbers, like really good actually.